Redistribution effects of water tariffs

Nguyen Bich Ngoc, Jacques Teller 24 December 2021

Household income histogram

inccatdf <- df[!(is.na(df$inccat)),] %>%
  group_by(inccat) %>%
  summarise(
    count = n(),
    prop = n() / nrow(df),
    income_avr = mean(income),
    income_min = min(income),
    income_max = max(income),
    inceqa_avr = mean(inceqa),
    inceqa_min = min(inceqa),
    inceqa_max = max(inceqa)
  )

inccatdf
## # A tibble: 4 x 9
##   inccat     count   prop income_avr income_min income_max inceqa_avr
##   <fct>      <int>  <dbl>      <dbl>      <int>      <int>      <dbl>
## 1 precarious   164 0.0949      1144.        125       2250      8928.
## 2 modest       781 0.452       1894.       1250       3250     16022.
## 3 average      566 0.328       3216.       2250       4750     23687.
## 4 higher       216 0.125       4759.       3750       5250     30933.
## # ... with 2 more variables: inceqa_min <dbl>, inceqa_max <dbl>

Household income histogram Household income histogram

Utilities Number of households CVD CVA Average price Block 1 price Block 2 price Block2/Block1
SWDE 1308 2.4480 1.745 4.7061 1.2974 4.4446 3.4257
CILE 277 2.6366 1.745 4.9270 1.3974 4.6445 3.3237
inBW 143 2.1600 1.745 4.3268 1.1448 4.1393 3.6157
## Warning: Removed 192 row(s) containing missing values (geom_path).

## Warning: Removed 150 row(s) containing missing values (geom_path).

Household income quintile characteristics
Quintile Number of households Number of people Min income (EUR/month) Max income (EUR/month)
1 346 550 125 1750
2 346 695 1750 2250
3 346 823 2250 2750
4 345 967 2750 3750
5 345 1156 3750 5250

Income per equivalent adults for different household income group Income per equivalent adults for different household income group

# Correlation between water consumption and household income should use spearman?????

cor.test(df$csmptv, df$income, method = "pearson")
## 
##  Pearson's product-moment correlation
## 
## data:  df$csmptv and df$income
## t = 15.729, df = 1726, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.3121384 0.3946480
## sample estimates:
##      cor 
## 0.354082
cor.test(df$csmptv, df$income, method = "spearman")
## Warning in cor.test.default(df$csmptv, df$income, method = "spearman"):
## Cannot compute exact p-value with ties

## 
##  Spearman's rank correlation rho
## 
## data:  df$csmptv and df$income
## S = 536353649, p-value < 2.2e-16
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##       rho 
## 0.3763062
# Correlation between water consumption and income per equivalent adult should use spearman?????

cor.test(df$csmptv, df$inceqa, method = "pearson")
## 
##  Pearson's product-moment correlation
## 
## data:  df$csmptv and df$inceqa
## t = 1.4269, df = 1726, p-value = 0.1538
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.01285165  0.08134826
## sample estimates:
##        cor 
## 0.03432454
cor.test(df$csmptv, df$inceqa, method = "spearman")
## Warning in cor.test.default(df$csmptv, df$inceqa, method = "spearman"):
## Cannot compute exact p-value with ties

## 
##  Spearman's rank correlation rho
## 
## data:  df$csmptv and df$inceqa
## S = 803896839, p-value = 0.006707
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##        rho 
## 0.06519613

Proportion of household paying in which block by quantile Proportion of household paying in which block by income quintile and utilities

summary(df$avrprc)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   4.200   4.595   4.690   5.000   4.850  12.549
summary(df$avrprc[df$poorest == 1])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## 
summary(df$avrprc[df$inccat == "precarious"])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   4.222   4.605   4.734   5.152   4.915  10.592       1
summary(df$subs[df$inccat == "precarious"])
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
## -68.1035  -4.7756   0.5658  -4.0094   8.3744  27.4910        1
summary(df$mgnprc)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.158   4.458   4.458   3.982   4.458   4.658
summary(df$mgnprc[df$poorest == 1])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## 
summary(df$mgnprc[df$inccat == "precarious"])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   1.158   4.153   4.458   3.754   4.458   4.658       1
## 3.5. changing fixed  -----

### new cvd ------
CVD_SWDE CVD_CILE CVD_inBW CVA scenario fixed rwtt mgpr_bl1 mgpr_bl2
2.4480 2.6366 2.1600 1.745 As in 2014 101.438 0 1.2272 4.1994
4.2744 4.5442 3.6839 1.745 1 0.000 0 2.1344 6.0138
3.3730 3.6365 2.8864 1.745 2 50.000 0 1.6875 5.1200
2.4716 2.7289 2.0890 1.745 3 100.000 0 1.2406 4.2262
1.5702 1.8212 1.2916 1.745 4 150.000 0 0.7937 3.3324
0.6688 0.9135 0.4942 1.745 5 200.000 0 0.3468 2.4386

CVD_SWDE CVD_CILE CVD_inBW CVA scenario fixed rwtt mgpr_bl1 mgpr_bl2
2.4480 2.6366 2.1600 1.745 6 101.4380 0 1.2272 4.1994
2.1517 2.4683 1.8852 1.745 7 95.9578 50 1.0902 3.9254
1.8554 2.3000 1.6104 1.745 8 90.4777 100 0.9532 3.6514
1.5591 2.1318 1.3356 1.745 9 84.9975 150 0.8162 3.3774
1.2628 1.9635 1.0608 1.745 10 79.5174 200 0.6792 3.1034

bl1_SWDE bl1_CILE bl1_inBW fixed revincr mgpr_bl1 mgpr_bl2
4.427231 4.635599 4.069370 0 0.0 4.431018 4.431018
3.710676 3.914732 3.423071 50 0.0 3.719586 3.719586
2.994121 3.193866 2.776773 100 0.0 3.008154 3.008154
5.315177 5.565218 4.885744 0 0.2 5.319721 5.319721
4.598622 4.844352 4.239446 50 0.2 4.608289 4.608289
3.882067 4.123486 3.593147 100 0.2 3.896857 3.896857
6.647096 6.959648 6.110305 0 0.5 6.652777 6.652777
5.930541 6.238782 5.464006 50 0.5 5.941345 5.941345
5.213986 5.517916 4.817708 100 0.5 5.229913 5.229913
bl1_SWDE bl1_CILE bl1_inBW fixed revincr mgpr_bl1 mgpr_bl2
1.789001 1.876093 1.594286 0 0.0 1.786848 6.253969
1.499448 1.584348 1.341081 50 0.0 1.499952 5.249832
1.209895 1.292603 1.087876 100 0.0 1.213056 4.245695
2.147811 2.252323 1.914122 0 0.2 2.145226 7.508291
1.858259 1.960578 1.660917 50 0.2 1.858330 6.504154
1.568706 1.668834 1.407712 100 0.2 1.571433 5.500017
2.686027 2.816669 2.393877 0 0.5 2.682792 9.389773
2.396474 2.524924 2.140672 50 0.5 2.395896 8.385636
2.106921 2.233179 1.887467 100 0.5 2.109000 7.381499
bl1_SWDE bl1_CILE bl1_inBW fixed revincr mgpr_bl1 mgpr_bl2
1.816621 1.870233 1.608886 0 0.0 1.808024 6.328085
1.522598 1.579399 1.353362 50 0.0 1.517698 5.311944
1.228575 1.288566 1.097838 100 0.0 1.227372 4.295803
2.180972 2.245288 1.931651 0 0.2 2.170649 7.597272
1.886948 1.954454 1.676128 50 0.2 1.880323 6.581131
1.592925 1.663621 1.420604 100 0.2 1.589997 5.564990
2.727497 2.807871 2.415800 0 0.5 2.714586 9.501052
2.433473 2.517037 2.160276 50 0.5 2.424260 8.484911
2.139450 2.226204 1.904752 100 0.5 2.133934 7.468770